Surrogate Outcome Regression Analysis

Performs estimation and inference on a partially missing target outcome while borrowing information from a correlated surrogate outcome to increase estimation precision and improve power. The target and surrogate outcomes are jointly modeled within a bivariate outcome regression framework. Unobserved values of either outcome are regarded as missing data. Estimation in the presence of bilateral outcome missingness is performed via an expectation conditional maximization algorithm. A flexible association test is provided for evaluating hypotheses about the target regression parameters. See McCaw ZR, Gaynor SM, Sun R, Lin X; “Cross-tissue eQTL mapping in the presence of missing data via surrogate outcome analysis” .


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install.packages("SurrogateRegression")

0.5.0 by Zachary McCaw, 8 months ago


Browse source code at https://github.com/cran/SurrogateRegression


Authors: Zachary McCaw [aut, cre]


Documentation:   PDF Manual  


GPL-3 license


Imports methods, mvnfast, plyr, Rcpp, stats

Suggests knitr, rmarkdown

Linking to Rcpp, RcppArmadillo


See at CRAN